Title: Features based on intrinsic mode functions for classification of EMG signals

Authors: Varun Bajaj; Anil Kumar

Addresses: Discipline of Electronics and Communication Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing Jabalpur, Jabalpur 452005, Madhya Pradesh, India ' Discipline of Electronics and Communication Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing Jabalpur, Jabalpur 452005, Madhya Pradesh, India

Abstract: In this paper, the features based on Intrinsic Mode Functions (IMFs) for classification of EMG signals are presented. The EMD method decomposes EMG signals into a set of narrow-band components known as IMFs. The features, namely mean frequency estimation and singular value computation, extracted from IMFs are exploited for classification of EMG signals. These parameters are used as an input to Least Squares Support Vector Machine (LS-SVM) with Radial Basis Function (RBF) for automatic classification of EMG signals. The classification accuracy for classification of normal and abnormal EMG signals obtained by the proposed method is 99.03% with RBF kernel of LS-SVM. The experimental results are presented to show the effectiveness of the proposed method for classification of normal and abnormal EMG signals (myopathy and neuropathy).

Keywords: electromyograms; EMG signals; LS-SVM; least squares; support vector machines; SVM; myopathy; neuropathy; biomedical engineering; intrinsic mode functions; feature extraction; radial basis function; RBF; signal classification.

DOI: 10.1504/IJBET.2015.070035

International Journal of Biomedical Engineering and Technology, 2015 Vol.18 No.2, pp.156 - 167

Received: 19 Nov 2014
Accepted: 23 Jan 2015
Published online: 25 Jun 2015 *

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